Kathleen F. Kerr Selected publications appear below. A comprehensive list of publications is available as an NCBI Collection.

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Research

Evaluating Biomarkers and Risk Prediction Models

I have a strong interest in advancing the rigorous evaluation of new biomarkers, including assessing a biomarker's incremental value to improve upon an exising risk predicition model.

• Kerr KF and Janes H. First things first: risk model performance metrics shoudl reflect the clinical application. Statistics in Medicine, 2017.
• Kerr KF, Wang Z, Janes H, McClelland R, Psaty B, Pepe M. Net Reclassification Indices for Evaluating Risk Prediction Instruments: A Critical Review. Epidemiology, 25:114-21, 2014. (Published with Invited Commentary.)
• Pepe MS, Kerr KF, Longton G, Wang Z: Testing for improvement in prediction model performance. Statistics in Medicine 32:1467-1482, 2013.
• Kerr KF, Bansal A, Pepe MS: Further Insight into the Incremental Value of New Markers: The Interpretation of Performance Measures and the Importance of Clinical Context. American Journal of Epidemiology, 2012.
• Kerr KF, Pepe MS: Joint Modeling, Covariate Adjustment, and Interaction: Contrasting Notions in Risk Prediction Models and Risk Prediction Performance. Epidemiology 22(6):805-812, 2011.
• Kerr KF, McClelland RL, Brown ER, Lumley T: Evaluating the Incremental Value of New Biomarkers with Integrated Discrimination Improvement. American Journal of Epidmiology, 2011.

Evaluating Risk Prediction Models for Risk-Based Treatment Recommendations

A guiding theme of my biomarker research is that biomarkers should be evaluated in a way that reflects how they will be used for practice. An important development in the past decade has been methods that use classical results from decison theroay to evaluating risk prediction models.

• Kerr KF, Brown MD, Zhu K, Janes H. Assessing the Clinical Impact of Risk Prediction Models With Decision Curves: Guidance for Correct Interpretation and Appropriate Use. Journal of Clinical Oncology 34:2534-2540, 2016.
• Kerr KF, Brown MD, Marsh TL, Janes H. Evaluating Risk Prediction Models for Opting Out of Treatment. Medical Decision Making 39:86-90, 2019.

• Software:

  rmda: Risk Model Decision Analysis. This R package supports Decision Curves and Relative Utliity Curves and supports the evaluating of risk models for both "opt in" and "opt out" treatment policies. (Author: Marshall Brown)

Recalibrating Risk Prediction Models

When a risk model will be used to recommend for or against treatment based on comparing an individual's estimated risk to a risk threshold, then good model calibration is especially critical near the critical risk threshold.

Mishra A, McClelland RL, Inoue LYT, Kerr KF. Recalibration Methods for Improved Clinical Utility and Clinically Relevant Calibration of Risk Scores. Medical Decision Making, 2021.

• Software:

  ClinicalUtilityRecal This R package implements methods for recalibrating a risk model when a primary purpose of the risk model is for risk-based decision-making. (Author: Anu Mishra)
  

Multi-Center Biomarker Studies

We have considered particular issues that can arise when biomarkers are studied in a mult-center setting. Most often the multi-center nature of the data is ignored, which can be problematic. We have proposed using center-adjusted measures of biomarker performance for multi-center biomarker studies.

• Meisner A, Parikh CR, Kerr KF. Biomarker Combinations for Diagnosis and Prognosis in Multicenter Studies: Principles and Methods. Statistical Methods in Medical Research, 2017.
• Meisner A, Parikh CR, Kerr KF. Developing Biomarker Combinations in Multicenter Studies via Direct Maximization and Penalization. Statistics in Medicine 39:3412-3426, 2020.

• Software:

  maxadjAUC: Maximizing the Adjusted AUC. This R package estimates a linear combination of biomarkers or other predictors by maximizing a smooth approximation to the estimated covariate-adjusted area under the receivor operating characteristic curve for a discrete covariate.). (Author: Allison Meisner)
  

Developing Biomarker Combinations

Some lines of research have pursued developing biomarker combinations by maximizing measures of performance such as AUC rather than using likelihood-based methods. However, AUC is not a clinically relevant measure of predictive performance. We have developed methods to develop biomarker combinations by maximizing the true positive rate while fixing the false positive rate, which is often easier to match to an intended application.

• Meisner A, Carone M, Pepe M, Kerr KF. Combining Biomarkers by Maximizing the True Positive Rate for a Fixed False Positive Rates. Biometrical Journal 63:1223-1240, 2021.

• Software:

  maxTPR: Maximizing the TPR for a Specified FPR. This R package estimates a linear combination of biomarkers or other predictors by maximizing a smooth approximation to the estimated true positive rate (TPR; sensitivity) while constraining a smooth approximation to the estimated false positive rate (FPR; 1-specificity). (Author: Allison Meisner)

Evaluating Biomarkers for Prognostic Enrichment of Clinical Trials

Clinical trials of interventions intended to prevent an unwanted clinical event may be conducted in a subset of the relevant patient population at highest risk of the event. We have considered how a biomarker should be evaluated when its purpose is to enrich a clinical trial in this way, termed "prognostic enrichment."

• Kerr KF, Roth J, Zhu K, Thiessen-Philbrook H, Meisner A, Wilson FP, Coca S, Parikh C. Evaluating biomarkers for prognostic enrichment of clinical trials Clinical Trials, 2017
• Cheng S, Kerr KF, Thiessen-Philbrook H, Coca S, Parikh CR. BioPETsurv: Methodology and open source software to evaluate biomarkers for prognostic enrichment of time-to-event clinical trials. PLoS One, 2020.

• Software:

  BioPET: Biomarker Prognostic Enrichment Tool. This R package evaluates the utility of a biomarker for prognostic enrichment of a clinical trials. Biomarkers are evaluated along several dimensions including the trial sample size, calendar time to enroll the trial, and total of patient costs including the cost of running a patient through the trial and the cost of biomarker screening. (Authors: Jeremy Roth, Kathleen Kerr, Kehao Zhu)
  BioPETsurv: Biomarker Prognostic Enrichment Tool for Time-to-Event Trial. Biomarker prognostic enrichment tool focused on trials with survival or time-to-event endpoints. (Author: Si Cheng)
  BioPET webtool offers easy access to a first-pass analysis of whether a biomarker is potentially useful for prognostic enrichment. (Authors: Jeremy Roth, Si Cheng)

Microarrays, Gene Expression, Bioinformatics

I was an eary developer of statistical methodology for gene experssion microarryas. We emphasized the importance of sound experiemtnal design for microarray studies.

• Kerr KF: Comments on the Analysis of Unbalanced Microarray Data. Bioinformatics, 25: 2035-2041, 2009.
• Kerr KF: Extended analysis of benchmarker datasets for Agilent two-color microarrays. BMC Bioinformatics 8:371, 2007.
• Qin, L-X, Kerr KF, Contributing Members of the Toxicogenomics Research Consortium: Empirical evaluation of data transformations and ranking statistics for microarray analysis. Nucleic Acids Research 32:5471-5479, 2004.
• Kerr MK: Linear models for microarray data analysis: Hidden similarities and differences, Journal of Computational Biology 10:891-901, 2003.
• Kerr MK: Design considerations for efficient and effective microarray studies, Biometrics 59:822-828, 2003.
• Kerr MK, Churchill GA: Experimental design for gene expression microarrays. Biostatistics 2:183-201, 2001.
• Kerr MK, Churchill GA: Statistical design and the analysis of gene expression microarrays. Genetical Research 77:123-128, 2001.
• Kerr MK, Martin M, Churchill GA: Analysis of variance for gene expression microarray data. Journal of Computational Biology 7:819-837, 2000.

Experimental Design

Through my work on two-color microarrays I have contributed to the body of knowledge on incomplete block designs.

• Kerr KF: Optimality Criteria for the Design of 2-Color Microarray Studies. Statistical Applications in Genetics and Molecular Biology 11:1-9, 2012.
• Kerr KF: 2^k Factorials in Blocks of Size 2, with Application to Two-Color Microarray Experiments. Journal of Quality Technology 38:349-364, 2006.

GWAS Methods

I have been involved in dozens of genomewide associations studies (GWAS) for cardiovascular and other traits. I have helped develop methodology related to GWAS in diverse (non-European ancestry) populations.

• Sofer T, Heller R, Bogomolov M, Avery C, Graff M, North K, Reiner A, Thornton T, Rice K, Banjamini Y, Laurie CC, Kerr KF. A powerful statistical framework for generalization testing in GWAS, with application to the HCHS/SOL. Genetic Epidemiology 41:251-250, 2017.
• Conomos MP, Laurie CA, Stilp AM, Gogarten SM, McHugh CP, Nelson SC, Sofer T, Fernández-Rhodes L, Justice AE, Graff M, Seyerle AA, Avery CL, Taylor KD, Rotter JI, Talavera GA, Daviglus ML, Wassertheil-Smoller S, Schneiderman N, Heiss G, Kaplan RC, Franceschini N, Reiner AP, Shaffer JR, Barr1 RG, Kerr KF, Browning SR, Browning BL, Weir BS, Avilés-Santa ML, Papanicolaou1 JG, Lumley T, Szpiro AA, North KE, Rice K, Thornton TA, and Laurie CC. Genetic Diversity and Association Studies in U.S. Hispanic/Latino Populations: Applications in the Hispanic Community Health Study/Study of Latinos. American Journal of Human Genetics, 2016.

Select Scientific Collaborations

• Smith JG, Luk K, Schulz C-A, Engert JC, Do R, Hindy G, Rukh G, Dufresne L, Almgren P, Owens DS, Harris TB, Peloso GM, Kerr KF, Wong Q, Smith AV, Rotter JI, Cupples A, Rich S, Kathiresan S, Orho-Melander M, Gudnason V, O’Donnell CJ, Post WS, Thanassoulis G. Association of Low-Density Lipoprotein Cholesterol–Related Genetic Variants With Aortic Valve Calcium and Incident Aortic Stenosis. Journal of the American Medical Association, 2014.
• den Hoed M, Eijgelsheim M, Esko T, Brundel B, Peal DS, Evans DM, Nolte IM, Segrè AV, Holm H, Handsaker RE, Westra H-J, Johnson T, Isaacs A, Yang J, Lundby A, Kim YJ, Go MJ, Almgren P, Bochud M, Boucher G, Cornelis MC, Gudbjartsson D, Hadley D, van der Harst P, Hayward C, den Heijer M, Igl W, Jackson AU, Kutalik Z, Luan J, Kemp JP, Kristiansson K, Ladenval C, Lorentzon M, Montasser ME, Njajou OT, O’Reilly PF, Padmanabhan S, St Pourcain B, Rankinen T, Salo P, Tanaka T, Timpson NJ, Vitart V, Waite L, Wheeler W, Zhao JH, Zhang W, Draisma HHM, Feitosa MF, Kerr KF, et al: Heart rate-associated loci and their effects on cardiac conduction and rhythm disorders. Nature Genetics 45:621-631, 2013.
• Thanassoulis G, Campbell CY, Owens DS, Smith JG, Smith AV, Peloso GM, Kerr KF, Pechlivanis S, Budoff MJ, Harris TB, Malhotra R, O’Brien KD, Allison MA, Aspelund T, Carr J, Criqui MH, Heckbert SR, Hwang S-J, Kathiresan S, Liu Y, Sjogren M, Van Der Pals J, Kälsch S, Cupples LA, Caslake M, Di Angelantonio E, Danesh J, Rotter JI, Sigurdsson S, Wong Q, Erbel R, Melander O, Gudnason V, O’Donnell CJ, Post WS for the CHARGE Extracoronary Calclum Working Group: Genetic associations with valvular calcification and aortic stenosis. New England Journal of Medicine 368:503-512, 2013.
• Yuan Z-C, Edlind MP, Liu P, Saenkham P, Banta LM, Wise AA, Ronzone E, Binns AN, Kerr K, Nester EW: The plant signal salicylic acid shuts down expression of the vir regulon and activates quormone-quenching gene in Agrobacterium. Proceedings of the National Academy of Sciences of the USA 104:11790-11795, 2007.
• Kerr KF, Morenz ER, Thiessen-Philbrook H, Coca SG, Wilson FP, Reese PP, Parikh CR. Quantifying Donor Effects on Transplant Outcomes Using Kidney Pairs from Deceased Donors. Clinical Journal of the American Society of Nephrology, 2019.
• Creevy KE, Akey JM, Kaeberlein M, Promislow DEL, Dog Aging Project Consortium. The Dog Aging Project: An Open Science Study of Aging in Companion Dogs. Nature 602:51-57, 2022.

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